16 results about "List mode" patented technology

The application particularly relates to a kind of asset performance analysis methods based on self-defined algorithm.The asset performance analysis method based on self-defined algorithm is through interface management algorithm factor, sets the mapping relationship of algorithm factor and collected data, and collects and stores algorithm factor data, presents algorithm factor index data in list mode;Algorithm formula is self-defined and set and legality check;Set early warning rule;If the data of current granularity has completed data collection in this period, then trigger algorithm scheduling;Algorithm calculation is carried out based on algorithm factor data;Automatic trigger early warning analysis, generate early warning message according to early warning rule, notify relevant person in charge.The asset performance analysis method based on self-defined algorithm solves the problem that analysis algorithm basic data cannot be quickly expanded, so that the expansion of basic data can be directly applied to algorithm, which can quickly realize the expansion and adjustment of algorithm, and also provides an effective means for the optimization and fine adjustment of algorithm.

1. Name of the Design Product: Graphical User Interface of Surveying and Mapping Project List for Electronic Devices. 2. Use of the Design Product: An electronic device. 3. Design Key Points of the Design Product: Lies in the graphical user interface on the screen. 4. Picture or Photo that Best Illustrates the Design Key Points: Interface Variation State Diagram 2. 5. The carrier is of conventional design, and the rear view, left view, right view, top view and bottom view are omitted. 6. Use of the Graphical User Interface: For operating the approval process and displaying process diagrams of each process. 7. Other Situations Requiring Explanation - Reference Diagram Explanation: Click "Pending Business" in the main view to enter Interface Variation State Diagram 1 (hereinafter referred to as Diagram); click "List Mode" in Diagram 1 to enter Diagram 2; click "Business Information Approval" in Diagram 2 to enter Diagram 3; click "Save" after entering information in Diagram 3 to enter Diagram 4; click "OK" in Diagram 4 to enter Diagram 5; click "OK" in Diagram 5 to enter Diagram 6; click "Save" after entering information in Diagram 6 to enter Diagram 7; click "OK" in Diagram 7 to enter Diagram 8; click "OK" in Diagram 8 to enter Diagram 9; click "Handling Process" in Diagram 9 to enter Diagram 10; click "List Mode" in Diagram 10 to enter Diagram 11. Among them, the digital information shown in each interface and the places referred to by X / Y / M / D are variable and do not belong to the design elements; the partial content shown by the gray color block in the main view is variable and does not belong to the design elements.

The invention discloses a browser-based data security processing method and system, and belongs to the technical field of data processing. According to the method, a hook program is implanted into a browser rendering pipeline; detecting data to be transmitted through a data transmission operation performed by the browser through the hook program; determining a control used for transmitting the data in a page displayed by the browser through the hook program, and forbidding a function of the control through the hook program; detecting the data, and keeping the data transmission function of the control to be disabled under the condition that the data is determined to comprise the sensitive content; and under the condition that the data does not comprise the sensitive content, recovering the data transmission function of the control. According to the method and the device, the problem caused by limiting the use lock of the user on the browser in a black and white list mode in order to ensure the data security in the prior art is solved, so that the data security is ensured to a certain extent while the user freely uses the browser.

Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. In the method, for a conversion between a current video block of a video and a bitstream of the video, whether a cross component prediction (CCP) candidate list mode is applicable to the current video block is determined based on coding information of at least one of: a current chroma block, or a collocated luma block. The conversion is performed based on the determining.

The invention discloses a list mode 3D PET image reconstruction method based on a learnable gradient descent algorithm, the list mode is directly used for image reconstruction, and compared with a traditional method for reconstruction through sinogram data, the method occupies less memory and can integrate flight time difference information. Therefore, the data processing efficiency is improved, the requirement for hardware resources is lowered, and the method is more suitable for the resource-limited environment. Meanwhile, the method also has the capability of reconstructing a 3D PET image, and compared with a 2D list mode reconstruction method, the method has the advantages that video memory occupation and model parameter quantity are optimized, and the method can be operated on an existing hardware platform. The method is unique in that the method does not need to depend on labeled training data, greatly reduces the difficulty of medical image data acquisition, and can learn the internal structure and characteristics of the image from non-labeled data, thereby reducing the threshold in practical application.

A list mode image reconstruction method includes a step of dividing list mode data into a plurality of subsets and a step of acquiring a subset balance coefficient based on the number of events in the plurality of subsets.

Embodiments of the present disclosure provide a solution for video processing. A method for video processing is presented. In the method, for a conversion between a current video block of a video and a bitstream of the video, whether a cross-component prediction (CCP) candidate list mode is applicable to the current video block is determined based on coding and decoding information of at least one of: a current chroma block, or a co-luma block. The conversion is performed based on the determination.

The application discloses a PET reconstruction model construction method based on list data and deep learning, which combines list mode data and deep learning counting through a domain conversion function, and solves the problem that list mode data is difficult to be directly processed through a deep neural network. Through the domain conversion function, the application can convert list mode data of any format and any storage size from a measurement domain to an image domain, so that an end-to-end deep neural network method can be directly used for processing and learning. The application uses deep learning to reconstruct list mode data, and compared with other methods for reconstructing list mode data, the reconstruction speed is greatly improved; for a list mode MLEM algorithm, it takes tens of seconds to reconstruct a brain image, and the application only needs about one second to complete the reconstruction, so that the application has greater application potential in the fields of real-time reconstruction and short-time frame reconstruction.

Systems and methods for estimating mean randomness include: acquiring list pattern data describing true and delayed coincidences detected by a positron emission tomography (PET) scanner during a scan of an object; determining, based on the list pattern data, delayed coincidences including said crystals for each crystal of the PET scanner and for each of a plurality of time periods of the scan; for each crystal, determining a single event rate associated with each time period based on the delayed coincidences determined for the crystal within the time period; for each time period, determining an estimated mean randomness for each of a plurality of crystal pairs based on the single event rate associated with the time period for each crystal of the crystal pair; and reconstructing an image of the object based on the estimated mean randomness for each time period and the detected true coincidences.

A nuclear medicine diagnosis apparatus according to an embodiment includes processing circuitry configured: to generate a feature vector for each of the mini frames from list mode data acquired by performing a nuclear medicine scan and to calculate a Euclidean distance between each of the feature vectors corresponding to frames and a reference phase vector; to calculate, on the basis of the Euclidean distances, a mean Euclidean distance for a plurality of conditions obtained by varying a percentage of counts to be used in a reconstructing process and to determine the percentage on the basis of a relationship between the percentage and the mean Euclidean distance; and to generate a gated image by carrying out an image reconstruction while using the determined percentage as a gating condition.

A PET apparatus according to one embodiment includes processing circuitry. The processing circuitry acquires list mode data. The processing circuitry reconstructs a PET image based on the list mode data. The processing circuitry identifies first coincidence data that is obtained by coincidence counting by a first detector and a second detector that is different from the first detector from among a plurality of pieces of coincidence data included in the list mode data. The processing circuitry estimates a time lag amount between the first detector and the second detector based on TOF information included in the first coincidence data and the PET image.

A computer-implemented method for motion compensation of medical imaging data includes estimating, via a processor, non-periodic motion of a myocardium of a heart due to respiration and / or other body movements throughout a positron emission tomography (PET) scan based on list-mode emission data acquired during the PET scan of the heart of a subject. The method also includes performing, via the processor, event-by-event motion-corrected list-mode reconstruction on the list-mode emission data to generate cardiac images with the non-periodic motion removed.

A radiation detection device includes a gamma detector configured to receive a stream of non-random and random photon events and generate corresponding list-mode data, and a computing device in communication with the gamma detector configured to receive the list-mode data, determine, by applying at least one statistical timing analysis method to a segment of the list-mode data, data associated with the non-random photon events, predict, based on the data, a next non-random time window, and reject photon events that have arrival times, at the gamma detector, in the predicted non-random time window.

The invention discloses a light equipment management method and device, a computer readable medium and electronic equipment, and the method comprises the steps: responding to a selection operation of a list mode, and displaying a list region, a control region and a preset mode region on a display interface at the same time; wherein the list area is used for displaying description information of light equipment needing to be managed in a list mode; the control area is used for displaying adjustable parameters of the light equipment; the preset mode area is used for displaying a pre-created scene control mode, and one scene control mode comprises at least one lighting device and a preset adjustment parameter of the lighting device. According to the technical scheme, the equipment list, the adjustment parameters, the scene mode and other information are synchronously displayed on the same interface, frequent jumping and switching among different function pages by a user are avoided, the information searching cost is reduced, and therefore the management efficiency of the lamplight equipment is improved.

A positron emission tomography (PET) system is provided. The system includes an image reconstruction computing device. The processors of the image reconstruction computing device are programmed to receive event data acquired by the PET system. The event data are represented as a list of coincidence events. The processors are also programmed to generate groups of coincidence events based on the event data, each group being representative of the event data. The processors are further programmed to perform a first level of image updates by iteratively updating a reconstructed image. Each image update is based on a first number of coincidence events. Further, the processors are programmed to perform a second level of image updates by iteratively updating the reconstructed image. Each image update is based on a second number of coincidence events. The first number of coincidence events is different from the second number of coincidence events.

The invention discloses a network scene anomaly detection algorithm based on a fault tree and IMS (IP Multimedia Subsystem) network operation and maintenance, which belongs to the field of network operation and maintenance, and comprises a fault tree framework of a minimum cutting set: taking a certain abnormal state of an operation and maintenance scene as a minimum cutting point, and according to a logic relationship of IMS network data transmission, performing downward decomposition step by step to obtain corresponding cutting sets, presenting the fault tree of the operation and maintenance scene in a list mode of a corresponding cut set; assuming that the system carries out state analysis, regarding a cut set as a minimum set when the fault tree is abnormal, and regarding a basic event set when one abnormal condition occurs in the fault tree as a cut set; according to the method, the abnormal data are obtained through the minimum cutting set framework based on the fault tree, the fault tree is drawn through the structure function, and the minimum cutting point is determined according to the logic relation of IMS network data transmission, so that the real-time abnormal situation can be predicted to be obtained through drawing the minimum cutting set; the operation process is low in energy consumption, and stability and efficiency are improved.